---
title: "Analysis of Alcohol Consumption Patterns"
output:
  flexdashboard::flex_dashboard:
    vertical_layout: scroll
    theme: bootstrap
    storyboard: true
    source_code: embed
    navbar:
      - { title: "Hayaku",  align: right}
      - { title: "Kexin Xu, Smriti Vinayak Bhat, Yalong liu, Yin Shan Ho",  align: right}
---
   

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(knitr)
library(tidytuesdayR)
library(tidyverse)
library(sf)
library(rnaturalearth)
library(gganimate)
library(viridis)
library(RColorBrewer)
library(rgeos)
library(gifski)
```

Introduction {.tabset}
======================

Column {.tabset}
-------------------------------------

### Background




Column {.tabset} {data-width=100}
-------------------------------------

### Approach of Project

The report will be collating specific sections of this data pertaining to countrywise policy responses, consumption pattern, youth and alcohol, levels of consumption and harms and consequences.

### Information on Data

This report is being created using the data from the GISAH (Global Information System on Alcohol and Health) tool of the World Health Organisation. GISAH tool helps monitor trends relating to alcohol consumption across various age groups, side effects (harm caused) as well as the policy responses by different countries.


Popularity {.tabset}
======================

Column {data-width=400}
-------------------------------------

### Total Consumption
```{r}
consumption <- read_csv("../clean_data/consumption_join.csv")
```

```{r} 
# Origin data pivot longer

b_tidy <- consumption %>%
  filter(Beverage_Types == "Spirits") %>%
  pivot_longer(cols = -c(Country, Beverage_Types),
               names_to = "Year",
               values_to = "Litres")
b_tidy
```

```{r}
# Loading countries data
countries <- ne_countries(returnclass = "sf", scale = "medium") %>%
  select(admin, continent, pop_est, gdp_md_est, economy, income_grp )
```



```{r}
# Test unmatched countries

anti_join(b_tidy, countries, by = c("Country" = "admin"))
```
```{r}
# Modify the country name consisting with rnaturalearth package

b_l_country <- b_tidy %>%
  mutate(Country = recode(Country,
                             "Bahamas" = "The Bahamas",
                             "Bolivia (Plurinational State of)" = "Bolivia",
                             "Brunei Darussalam" = "Brunei",
                             "Cabo Verde" = "Cape Verde",
                             "Congo" = "Democratic Republic of the Congo",
                             "Côte d'Ivoire" = "Ivory Coast",
                             "Czechia" = "Czech Republic",
                             "Democratic People's Republic of Korea" = "North Korea",
                             "Eswatini" = "Swaziland",
                             "Guinea-Bissau" = "Guinea Bissau",
                             "Iran (Islamic Republic of)" = "Iran",
                             "Lao People's Democratic Republic" = "Laos",
                             "Micronesia (Federated States of)" = "Federated States of Micronesia",
                             "North Macedonia" = "Macedonia",
                             "Republic of Korea" = "South Korea",
                             "Republic of Moldova" = "Moldova",
                             "Russian Federation" = "Russia",
                             "Serbia" = "Republic of Serbia",
                             "Syrian Arab Republic" = "Syria",
                             "Timor-Leste" = "East Timor",
                             "Tuvalu" = "Wallis and Futuna",
                             "United Kingdom of Great Britain and Northern Ireland" = "United Kingdom", 
                             "Venezuela (Bolivarian Republic of)" = "Venezuela",
                             "Viet Nam" = "Vietnam"))%>%
  filter(!is.na(Country))
```

```{r}
# Beer popularity by time

b_l_country_time <- left_join(countries, b_l_country, by = c("admin" = "Country"))

b_time  <- ggplot()+
  geom_sf(data = countries, fill = NA)+
  geom_sf(data = b_l_country_time %>% filter(!is.na(Year)), mapping = aes(fill = Litres))+
  scale_fill_viridis(na.value="white")+
  labs(title = "Countries where Beer is popular",
       subtitle = "Year: {current_frame}",
       fill = "Total")+
  theme_bw()+
  transition_manual(Year)


animate(b_time, duration = 30, fps = 20, width = 1000, height = 500, renderer = gifski_renderer())
```


### Total Percentage

*pie*

```{r}
consumption %>%
  select(Beverage_Types, "2018") %>%
  group_by(Beverage_Types) %>%
  summarise(no = sum(`2018`, na.rm = TRUE))
```

```{r}
# The number of litres consumed for each country

b_l_country <- b_tidy %>%
  group_by(Country)%>%
  summarise(Litres = sum(Litres, na.rm = TRUE))
b_l_country
```

%>%
  group_by(Country, Year)%>%
  summarise(Litres = sum(Litres, na.rm = TRUE))


### Research Question 1 

**How has the popularity of each type of alcohol changed over the years for different countries and have the local favorites of earlier decades been overtaken by other types of alcohol in terms of consumption?**

### Research Question 2 

**Does alcohol take more lives in traffic crashes or illnesses and which countries suffer more from alcohol-related cases?**

### Research Question 3

**What are the recurring patterns of policy responses across different countries in the world and how has it impacted the consumption of alcohol?**

### Research Question 4

**Do drinking age restrictions and legal blood alcohol concentration (BAC) limits for young drivers have any effect on adolescents’ drinking among countries?**

### Research Question 5

**What are the trends in youth drinking issues over the years?**


Findings {.tabset}
======================

Limitations {.tabset}
======================

Conclusion {.tabset}
======================

References {.tabset}
======================

GHO | global information system on alcohol and Health (gisah) | global information system on alcohol and health. (n.d.). Retrieved May 09, 2021, from https://apps.who.int/gho/data/node.gisah.GISAH?lang=en&showonly=GISAH